text: # copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  import paddle
  from paddle import nn
  import paddle.nn.functional as F
  
  
  class BalanceLoss(nn.Layer):
      def __init__(self,
                   balance_loss=True,
                   main_loss_type='DiceLoss',
                   negative_ratio=3,
                   return_origin=False,
                   eps=1e-6,
                   **kwargs):
          """
                 The BalanceLoss for Differentiable Binarization text detection
                 args:
                     balance_loss (bool): whether balance loss or not, default is True
                     main_loss_type (str): can only be one of ['CrossEntropy','DiceLoss',
                         'Euclidean','BCELoss', 'MaskL1Loss'], default is  'DiceLoss'.
                     negative_ratio (int|float): float, default is 3.
                     return_origin (bool): whether return unbalanced loss or not, default is False.
                     eps (float): default is 1e-6.
                 """
          super(BalanceLoss, self).__init__()
          self.balance_loss = balance_loss
          self.main_loss_type = main_loss_type
          self.negative_ratio = negative_ratio
          self.return_origin = return_origin
          self.eps = eps
  
          if self.main_loss_type == "CrossEntropy":
              self.loss = nn.CrossEntropyLoss()
          elif self.main_loss_type == "Euclidean":
              self.loss = nn.MSELoss()
          elif self.main_loss_type == "DiceLoss":
              self.loss = DiceLoss(self.eps)
          elif self.main_loss_type == "BCELoss":
              self.loss = BCELoss(reduction='none')
          elif self.main_loss_type == "MaskL1Loss":
              self.loss = MaskL1Loss(self.eps)
          else:
              loss_type = [
                  'CrossEntropy', 'DiceLoss', 'Euclidean', 'BCELoss', 'MaskL1Loss'
              ]
              raise Exception(
                  "main_loss_type in BalanceLoss() can only be one of {}".format(
                      loss_type))
  
      def forward(self, pred, gt, mask=None):
          """
          The BalanceLoss for Differentiable Binarization text detection
          args:
              pred (variable): predicted feature maps.
              gt (variable): ground truth feature maps.
              mask (variable): masked maps.
          return: (variable) balanced loss
          """
          # if self.main_loss_type in ['DiceLoss']:
          #     # For the loss that returns to scalar value, perform ohem on the mask
          #     mask = ohem_batch(pred, gt, mask, self.negative_ratio)
          #     loss = self.loss(pred, gt, mask)
          #     return loss
  
          positive = gt * mask
          negative = (1 - gt) * mask
  
          positive_count = int(positive.sum())
          negative_count = int(
              min(negative.sum(), positive_count * self.negative_ratio))
          loss = self.loss(pred, gt, mask=mask)
  
          if not self.balance_loss:
              return loss
  
          positive_loss = positive * loss
          negative_loss = negative * loss
          negative_loss = paddle.reshape(negative_loss, shape=[-1])
          if negative_count > 0:
              sort_loss = negative_loss.sort(descending=True)
              negative_loss = sort_loss[:negative_count]
              # negative_loss, _ = paddle.topk(negative_loss, k=negative_count_int)
              balance_loss = (positive_loss.sum() + negative_loss.sum()) / (
                  positive_count + negative_count + self.eps)
          else:
              balance_loss = positive_loss.sum() / (positive_count + self.eps)
          if self.return_origin:
              return balance_loss, loss
  
          return balance_loss
  
  
  class DiceLoss(nn.Layer):
      def __init__(self, eps=1e-6):
          super(DiceLoss, self).__init__()
          self.eps = eps
  
      def forward(self, pred, gt, mask, weights=None):
          """
          DiceLoss function.
          """
  
          assert pred.shape == gt.shape
          assert pred.shape == mask.shape
          if weights is not None:
              assert weights.shape == mask.shape
              mask = weights * mask
          intersection = paddle.sum(pred * gt * mask)
  
          union = paddle.sum(pred * mask) + paddle.sum(gt * mask) + self.eps
          loss = 1 - 2.0 * intersection / union
          assert loss <= 1
          return loss
  
  
  class MaskL1Loss(nn.Layer):
      def __init__(self, eps=1e-6):
          super(MaskL1Loss, self).__init__()
          self.eps = eps
  
      def forward(self, pred, gt, mask):
          """
          Mask L1 Loss
          """
          loss = (paddle.abs(pred - gt) * mask).sum() / (mask.sum() + self.eps)
          loss = paddle.mean(loss)
          return loss
  
  
  class BCELoss(nn.Layer):
      def __init__(self, reduction='mean'):
          super(BCELoss, self).__init__()
          self.reduction = reduction
  
      def forward(self, input, label, mask=None, weight=None, name=None):
          loss = F.binary_cross_entropy(input, label, reduction=self.reduction)
          return loss
  
  
  def ohem_single(score, gt_text, training_mask, ohem_ratio):
      pos_num = (int)(np.sum(gt_text > 0.5)) - (
          int)(np.sum((gt_text > 0.5) & (training_mask <= 0.5)))
  
      if pos_num == 0:
          # selected_mask = gt_text.copy() * 0 # may be not good
          selected_mask = training_mask
          selected_mask = selected_mask.reshape(
              1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
          return selected_mask
  
      neg_num = (int)(np.sum(gt_text <= 0.5))
      neg_num = (int)(min(pos_num * ohem_ratio, neg_num))
  
      if neg_num == 0:
          selected_mask = training_mask
          selected_mask = selected_mask.reshape(
              1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
          return selected_mask
  
      neg_score = score[gt_text <= 0.5]
      # 将负样本得分从高到低排序
      neg_score_sorted = np.sort(-neg_score)
      threshold = -neg_score_sorted[neg_num - 1]
      # 选出 得分高的 负样本 和正样本 的 mask
      selected_mask = ((score >= threshold) |
                       (gt_text > 0.5)) & (training_mask > 0.5)
      selected_mask = selected_mask.reshape(
          1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
      return selected_mask
  
  
  def ohem_batch(scores, gt_texts, training_masks, ohem_ratio):
      scores = scores.numpy()
      gt_texts = gt_texts.numpy()
      training_masks = training_masks.numpy()
  
      selected_masks = []
      for i in range(scores.shape[0]):
          selected_masks.append(
              ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[
                  i, :, :], ohem_ratio))
  
      selected_masks = np.concatenate(selected_masks, 0)
      selected_masks = paddle.to_variable(selected_masks)
  
      return selected_masks
  